Statistical content block matching scheme for pre-processing in encoding and transcoding

ABSTRACT

Statistical content block matching for video pre-processing, for example in fast motion estimation, uses a second-order distortion criterion for processing steps such as identifying a best reference image portion for comparison with a current image portion. The second-order distortion criterion is a Lagrange-optimized combination of a mean squared error criterion with an entropy criterion. Then a fast motion estimation search advantageously includes performing a diamond search using the second-order distortion criterion to identify a candidate best reference image portion, and performing a nearest neighbor search starting using said second-order distortion criterion to identify the best reference image portion within a search range limited by an adaptive search range cap. A better motion vector can then be calculated.

RELATED APPLICATION

This application claims benefit under 35 U.S.C. §119 as a continuationof U.S. Provisional Application No. 60/656,663, filed Feb. 24, 2005.

FIELD OF THE INVENTION

This application outlines a new statistical content block matchingscheme for motion estimation, video temporal pre-processing,optimizations and applications in encoding and transcoding environments.

BACKGROUND OF THE INVENTION

Video and image pre-processing technology is used widely in videoprocessing, compression, broadcasting, storage and broadband networking,imaging, printing and other areas for achieving better quality andhigher efficiency. In the video coding and transcoding fields,pre-processing provides the advantages of obvious visual qualityenhancement and substantial compression efficiency by noise reductionand visual enhancement of corrupted or distorted video sequences fromthe capture source or transmission processes.

Pre-processing technology is also becoming necessary in videocompression, storage and transportation, because high qualitypre-processing provides a real differential and advantageoustechnological edge in terms of yielding both a high visual quality and ahigh compression ratio, particularly in the Video-over-IP, HDTV andrelated markets. To give a quantitative example, the advantageouspre-processing scheme in accordance with the present invention canresult in a visual quality improvement of about 3 dB in signal to noiseratio and about 50-60% in coding efficiency gain. This is an improvementeven greater than that achievable under the H.264 (Advanced VideoCoding) standard over the MPEG-2 standard.

Furthermore, due to the nature of video sequences, motion estimation hasbeen used in a variety of video processing and compression applications,including pre-processing. There are numerous motion estimationalgorithms known in the literature for image processing, visual noisereduction filtering and video compression. However, many of thesealgorithms are too expensive in computational complexity to beimplemented, or they are unsatisfactory in their performance. Indeed,some algorithms are both too expensive and unsatisfactory.

The new pre-processing optimization combined with later stage encodingor transcoding optimization in accordance with the statistical contentblock matching scheme of the present invention yields a more advancedand competitive approach.

The following is a brief overview of certain areas of informationforming the background to the present invention.

(A) Encoding

The present invention is advantageously embodied in a video encoder. Aconventional video encoder is preferably an encoder which utilizes avideo compression algorithm to provide, for example, an MPEG-2compatible bit stream. The MPEG-2 bit stream has six layers of syntax.These are a sequence layer (random access unit, context), Group ofPictures layer (random access unit, video coding), picture layer(primary coding layer), slice layer (resychronization unit), macroblock(motion compensation unit), and block layer (DCT unit). The encoderdistinguishes between three kinds of pictures, I (“intra”), P(“predictive”) and B (“bi-predictive”). A group of pictures (GOP) is aset of frames which starts with an I-picture and includes a certainnumber of P and B pictures. The number of pictures in a GOP may befixed. The coding of I pictures results in the greatest number of bits.In an I-picture, each 8×8 block of pixels is defined as a macroblock andundergoes a DCT transform to form an 8×8 array of transformcoefficients. The transform coefficients are then quantized with avariable quantizer matrix. The resulting quantized DCT coefficients arescanned using, e.g., zig-zag scanning, to form a sequence of DCTcoefficients. The DCT coefficients are then organized into run, levelpairs. The run, level pairs are then entropy encoded. In an I-picture,each macroblock is encoded according to this technique, which is knownas spatial encoding.

In a P-picture, a decision is made to code the macroblock as an Imacroblock or as a P macroblock. For each P macroblock, a prediction ofthe macroblock in a previous video picture is obtained. While thistechnique is discussed in more detail below, generally the predictionmacroblock is identified by a motion vector which indicates thetranslation between the macroblock to be coded in the current pictureand its “best match” prediction in a previous picture. The predictiveerror between the prediction macroblock and the current macroblock isthen coded using the DCT, quantization, scanning, run, level pairencoding, and entropy encoding.

In the coding of a B-picture, a decision has to be made as to the codingof each macroblock. The choices are (a) intracoding (as in an Imacroblock), (b) unidirectional backward predictive coding using asubsequent picture to obtain a motion compensated prediction, (c)unidirectional forward predictive coding using a previous picture toobtain a motion compensated prediction, and (d) bidirectional predictivecoding wherein a motion compensated prediction is obtained byinterpolating a backward motion compensated prediction and a forwardmotion compensated prediction. In the cases of forward, backward, andbidirectional motion compensated prediction, the predictive error isencoded using DCT, quantization, zig-zag scanning, run, level pairencoding, and entropy encoding.

B pictures have the smallest number of bits when encoded, then Ppictures, with I pictures having the most bits when encoded. Thus, thegreatest degree of compression is achieved for B pictures. For each ofthe I, B, and P pictures, the number of bits resulting from the encodingprocess can be controlled by controlling the quantizer step size. Amacroblock of pixels or pixel errors which is coded using a largequantizer step size results in fewer bits than if a smaller quantizerstep size is used. Other techniques may also be used to control thenumber of encoded bits.

(B) Motion Estimation

As indicated above, temporal encoding typically involves finding aprediction macroblock for each to-be-encoded macroblock. The predictionmacroblock is subtracted from the to-be-encoded macroblock to form aprediction error macroblock. The individual blocks of the predictionerror macroblock are then spatially encoded.

Each prediction macroblock originates in a picture other than theto-be-encoded picture, called a “reference picture.” A single predictionmacroblock may be used to “predict” a to-be-encoded macroblock ormultiple prediction macroblocks, each origination in a differentreference picture, may be interpolated, and the interpolated predictionmacroblock may be used to “predict” the to-be-encoded macroblock.Preferably, the reference picture, themselves, are first encoded andthen decompressed or “decoded.” The prediction macroblocks used inencoding are selected from “reconstructed pictures” produced by thedecoding process. Reference pictures temporally precede or succeed theto-be-encoded picture in the order of presentation or display. Based onthese reference pictures, the I, P and B encoded pictures may beproduced.

MPEG-2 supports several different types of prediction modes which can beselected for each to-be-encoded macroblock, based on the types ofpredictions that are permissible in that particular type of picture. Ofthe available prediction modes, two prediction modes are described belowwhich are used to encoded frame pictures. According to a “frameprediction mode” a macroblock of a to-be-encoded frame picture ispredicted by a frame prediction macroblock formed from one or morereference frames. For example, in the case of a forward only predictedmacroblock, the prediction macroblock is formed from a designatedpreceding reference frame. In the case of backward only predictedmacroblock, the prediction macroblock is formed from a designatedsucceeding reference frame. In the case of a bi-predicted macroblock,the prediction macroblock is interpolated from a first macroblock formedfrom the designated preceding reference frame and a second predictionmacroblock formed from the designated succeeding reference frame.

According to a “field prediction mode for frames” a macroblock of ato-be-encoded frame picture is divided into to-be-encoded top and bottomfield macroblocks. A field prediction macroblock is separately obtainedfor each of the to-be-encoded top and bottom field macroblocks. Eachfield prediction macroblock is selected from top and bottom designatedreference fields. The particular fields designated as reference fieldsdepend on whether the to-be-encoded field macroblock is the firstdisplayed field of a P-picture, the second displayed field of aP-picture or either field of a B-picture. Other well known predictionmodes applicable to to-be-encoded field pictures include dual prime,field prediction of field pictures and 16×8 prediction. For sake ofbrevity, these modes are not described herein.

Prediction macroblocks often are not at the same relative spatialposition (i.e., the same pixel row and column) in the reference pictureas the to-be-encoded macroblock spatial position in the to-be-encodedpicture. Rather, a presumption is made that each prediction macroblockrepresents a similar portion of the image as the to-be-encodedmacroblock, which image portion may have moved spatially between thereference picture and the to-be-encoded picture. As such, eachprediction macroblock is associated with a motion vector, indicating aspatial displacement from the prediction macroblock's original spatialposition in the reference field to the spatial position corresponding tothe to-be-encoded macroblock. This process of displacing one or moreprediction macroblocks using a motion vector is referred to as motioncompensation.

In motion compensated temporal encoding, the best predictionmacroblock(s) for each to-be-encoded macroblock is generally not knownahead of time. Rather, a presumption is made that the best matchingprediction macroblock is contained in a search window of pixels of thereference picture around the spatial coordinates of the to-be-encodedmacroblock (if such a prediction macroblock exists at all). Given amacroblock of size I×J pixels, and a search range of ±H pixelshorizontally and ±V pixels vertically, the search window is of size(1+2H)(J+2V). A block matching technique may be used, whereby multiplepossible prediction macroblock candidates at different spatialdisplacements (i.e., with different motion vectors) are extracted fromthe search window and compared to the to-be-encoded macroblock. The bestmatching prediction macroblock candidate may be selected, and itsspatial displacement is recorded as the motion vector associated withthe selected prediction macroblock. The operation by which a predictionmacroblock is selected, and its associated motion vector is determined,is referred to as motion estimation.

Block matching in motion estimation requires identifying the appropriatesearch window for each to-be-encoded macroblock (that can possibly betemporally encoded). Then multiple candidate macroblocks of pixels mustbe extracted from each search window and compared to the to-be-encodedmacroblock. According to the MPEG-2 chrominance format 4:2:0, forexample, each macroblock includes a 2×2 arrangement of four (8×8 pixel)luminance blocks (illustratively, block matching is performed only onthe luminance blocks). If each to-be-encoded picture is a CIF formatpicture (352×288 pixels for NTSC frames and 352×144 for NTSC fields),then the number of to-be-encoded macroblocks is 396 for frame picturesand 196 for each field picture. According to MPEG-2, the search rangecan be as high as ±128 pixels in each direction. Furthermore, considerthat MPEG-2 often provides a choice in selecting reference pictures fora to-be-encoded picture (i.e., a field-frame choice or a forward only,backward only or bi-predictive interpolated choice). In short, thenumber of potential candidate prediction macroblocks is very high. Anexhaustive comparison of all prediction macroblock candidates to theto-be-encoded macroblock may therefore be too processing intensive forreal-time encoding.

An exhaustive search can sometimes provide better memory accessefficiency due to the overlap in pixels in each prediction macroblockcandidate compared against a given to-be-encoded macroblock. Forexample, consider that a retrieved prediction macroblock candidate of16×16 pixels includes a sub-array of 15×16 pixels of the predictionmacroblock candidate to the immediate right or left (an of course asub-array of 16×15 pixels of the prediction macroblock candidateimmediately above or below). Thus only the missing 1×16 column of pixelsneed be retrieved to form the next left or right prediction macroblockcandidate (or the missing 1×16 row of pixels need be retrieved to formthe next above or below prediction macroblock candidate).

According to another technique, a hierarchical or telescopic search isperformed, in which fewer than all possible choices are examined. Thesetechniques, while computationally less demanding, are more likely tofail to obtain the optimal or best matching prediction macroblockcandidate. As a result, more bits may be needed to encode theto-be-encoded macroblock in order to maintain the same quality than inthe case where the best matching macroblock is obtained, or, if thenumber of bits per picture is fixed, the quality of the compressedpicture will be degraded. Note also that the memory access efficiency islower for the hierarchical search, since by definition, the amount ofoverlapping pixels between each prediction macroblock will be lower.

(C) Video Buffer Verifier

The encoding techniques described above produce a variable amount ofencoded data for each picture (frame or field) of the video signal. Theamount of encoded data produced for each picture depends on a number offactors including the amount of motion between the to-be-encoded pictureand other pictures used as references for generating predictionstherefor. For example, a video signal depicting a football game tends tohave high motion pictures and a video signal depicting a talk show tendsto have low motion pictures. Accordingly, the average amount of dataproduced for each picture of the football game video signal tends to behigher than the average amount of data produced for each picture ofcomparable quality of the talk show.

The allocation of bits from picture to picture or even within a picturemay be controlled to generate a certain amount of data for that picture.However, the buffer at the decoder has a finite storage capacity. Whenencoding a video signal, a dynamically adjusted bit budget may be setfor each picture to prevent overflow and underflow at the decoder buffergiven the transmission bit rate, the storage capacity of the decoderbuffer and the fullness of the decoder buffer over time. Note thatvarying the number of bits that can be allocated to a picture impactsthe quality of the pictures of the video signal upon decoding.

The bit budget is set to prevent a decoder buffer underflow or overflowgiven a certain transmission channel bit rate. In order to preventdecoder buffer underflow and overflow, the encoder models the decoderbuffer in order to determine the fullness of the decoder's buffer fromtime to time. The behavior of the decoder buffer is now considered ingreater detail.

In modeling the decoder buffer, the encoder determines the bufferfullness of the decoder buffer. The encoder can know how many bits arepresent in the decoder buffer given the allocated transmission channelbit rate at which such pictures are transmitted to the decoder buffer,the delay between encoding a picture at the encoder and decoding apicture at the decoder, and the knowledge that the decoder buffer isassumed to remove the next to be decoded picture instantaneously atprescribed picture intervals. The encoder attempts to determine eachmaximum and minimum of the decoder buffer's fullness, which correspondto the number of bits in the buffer immediately before the decoderremoves a picture and the number of bits in the buffer immediately afterthe decoder removes a picture, respectively. Given such information, theencoder can determine the number of bits to allocate to successivepictures to prevent decoder buffer underflows (when the decoder bufferdoes not have all of the bits of a picture in time for the decoder todecode them at a predefined decode time) or overflows (when the decoderbuffer fullness exceeds the maximum decoder buffer storage capacity).

(D) Resolution/Standards Conversion

The use of high resolutions, high bit rates and/or inter-frame encodingcan increase the difficulty of processing functions such as accessingstored compressed video streams, playing back more than one bit streamat the same time, and decoding/decompressing with trick modes such asfast forward and fast reverse. On the other hand, a compression systemwhich utilizes compressed video bit streams having low resolution, lowbit rate and/or only intra-frame encoding does not suffer thesedrawbacks. It is therefore desirable in many applications to provide asystem in which multiple resolution and/or multiple bit rate versions ofa given video signal can be compressed and stored. The high resolutions,high bit rates and inter-frame encoding can then be utilized whennecessary, while the advantages of low resolution, low bit rates andintra-frame encoding can also be provided in appropriate applications.

Video servers represent another application in which storage of multipleversions of compressed video bit streams is desirable. Such videoservers are used to deliver video bit streams to end users over datacommunication networks. For example, a World Wide Web server may be usedto deliver video bit streams to different end users over different typesof lines, including plain old telephone service (POTS) lines, integratedservices digital network (ISDN) lines, T1 lines and the like. A versionof a given compressed bit stream that may be suitable for a POTS userwould be considered poor quality by a T1 user, and a bit stream suitablefor a T1 user would be at too high a bit rate for a POTS user. It istherefore desirable for the video server to store a given video bitstream at multiple bit rates. The “optimal” resolution for a compressedvideo bit stream is the one that yields the best subjective videoquality after decompression. This optimal resolution generally decreaseswith bit rate, such that it is desirable for the video server tocompress the different bit rate streams at different resolutions.

The name of the process of converting a media file or object from oneformat to another is transcoding. Transcoding is often used to convertvideo formats (e.g., Beta to VHS, VHS to QuickTime, QuickTime to MPEGetc.). It can also be used in applications such as fitting HTML filesand graphics files to the unique constraints of mobile devices and otherWeb-enabled products.

(E) Re-Encoding

Many video encoding applications utilize statistical multiplexingtechniques to combine several compressed video bit streams into a singlemultiplexed bit stream, e.g., for transmission on a single channel. Thebit rate of a given compressed stream generally varies with time basedon the complexity of the corresponding video signals. A statisticalmultiplexer attempts to estimate the complexity of the various videoframe sequences of a video signal and allocates channel bits among thecorresponding compressed video bit streams so as to provide anapproximately constant level of video quality across all of themultiplexed streams. For example, a given video frame sequence with arelatively large amount of spatial activity or motion may be morecomplex than other sequences and therefore allocated more bits than theother sequences.

Some statistical multiplexers use only a priori statistics, while othersuse both a priori and a posteriori statistics in allocating availablechannel bits. A statistics gatherer and encoder element 72 receives nvideo signals. These a priori statistics may include pre-encodingstatistics gathered during the encoding of the respective video signal,or other a priori statistics (e.g., inter-pixel differences). Togenerate the a posterior statistics, the compressed video bit streamsand the a priori statistics are retrieved. A transcoder has a decoderportion which decodes the retrieved compressed video bit streams toreproduce the video signals and an encoder portion which re-encodes thereproduced video signals to produce re-compressed video signals. Inre-encoding the reproduced video signals, the transcoder gathers aposteriori statistics indicating the complexity involved in re-encodingthe reproduced video signals. These a posteriori statistics and the apriori statistics are used in allocating available channel bits toachieve a desired bit rate.

SUMMARY OF THE INVENTION

Thus, a comprehensive statistical content block matching scheme usable,for example, in a pre-processing motion estimation scheme is presented,including but not limited to the following innovative areas:

1. The combination and process of three major components: distortioncriterion, motion prediction, and hybrid search.

2. An optimization object function J(Δx, Δy) and MSE used incalculations for: optimal visual quality, unique application in variableblock size encoding in an encoder or transcoder, entropy calculationcombined with encoding bit rate control process, faster computation andimplementation.

3. A new three-level motion search process from coarse to fine: motionvector prediction as efficient initialization, first-step diamondsearch, and on-the-fly nearest neighbor search, which yields fasterresults than a logarithmic value of the search range and optimalperformance similar to the full search.

4. The unique idea of entropy reduction in motion estimation and itsimplementation.

5. The new idea and implementation of the combined motion estimation forboth pre-processing and encoding/transcoding.

6. On-the-fly neighboring motion (search) range adaptation to get aconsistent and high quality motion result and avoid unnecessarycomputations.

7. On-the-fly nearest neighbor search with on-the-fly pixelinterpolation for high motion vector accuracy at sub-pixel (fractionalvalue) levels for high quality video compression and processingapplications/markets.

8. The DSP implementation and optimization, memory architecture and dataflow, and other optimal implementation details.

In a preferred embodiment, a method of statistical content blockmatching for video pre-processing, comprising in accordance with thepresent invention comprises, in the recited order, the steps of:

-   -   First: (A) selecting a second-order distortion criterion for        identifying a best reference image portion for comparison with a        current image portion, said second-order distortion criterion        being a Lagrange-optimized combination of a mean squared error        criterion with an entropy criterion;    -   Second: (B) using said second-order distortion criterion to        select an initial reference image portion as an initial starting        point for a motion estimation search, said initial starting        position being chosen based on at least one motion vector from a        neighboring image portion; and    -   Third: (C) performing a fast motion estimation search including        the steps of:    -   Fourth: (i) performing a diamond search starting from said        initial reference image portion using said second-order        distortion criterion to identify a candidate best reference        image portion;    -   Fifth: (ii) performing a nearest neighbor search starting from        the candidate best reference image portion using said        second-order distortion criterion to identify either the        candidate best reference image portion or a different        neighboring reference image portion as a better reference image        portion within a search range limited by an adaptive search        range cap;    -   Sixth: (iii) re-identifying the better reference image portion        as the candidate best reference image portion;    -   Seventh: (iv) repeating said steps (ii) and (iii) until in said        step (ii) either (a) the candidate best reference image portion        is identified as the better reference image portion so that said        second-order distortion criterion cannot be improved, or (b) any        further search would exceed the adaptive search range cap;    -   Eighth: (v) identifying the candidate best reference image        portion as the best reference image portion; and    -   Ninth: (vi) calculating a motion vector from the best reference        image portion.

These and other aspects and features of the present invention aredescribed below in the following detailed description of certainpreferred embodiments, taken together with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a diamond search usable in a preferredembodiment of a fast search method in accordance with the presentinvention.

FIG. 2 is an illustration of a nearest neighbor search usable in apreferred embodiment of a fast search method in accordance with thepresent invention.

FIG. 3 is a conceptual block diagram of an iPlex™ motherboard as anadvantageous example of hardware usable for implementing aspects of thepresent invention including, but not limited to, the advantageousmethods and signals described below.

FIG. 4 is further block diagram of the iPlex™ Hardware Architecture.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Herein, an inventive statistical content block matching scheme embodiedin a motion estimation scheme for temporal pre-processing is outlinedfor high performance and real-time DSP implementation. The motionestimation scheme can also be utilized in a compatible way for bothpre-processing and later stage compression such as encoding andtranscoding, yielding even better results.

One advantageous embodiment of hardware for implementing any or all ofthe aspects of this inventive motion estimation scheme is illustrated inFIGS. 3 and 4. Especially as shown in FIG. 3, the hardware is embodiedin an iPlex™ motherboard (iPlex™ is a trademark of SkyStreamCorporation, assignee of the present application). The motherboard 100,which advantageously is powered by two Pentium® processors, includes aninput structure 102 for receiving signals including, but not limited to,video signals, and an output structure 104 for outputting signalsincluding, but not limited to, video signals.

The motherboard 100 advantageously includes a plurality of PCI MezzanineCards (“PCM”), such as PCM 106 and PCM 108. Each PCM 106, 108 is a PCI(“Peripheral Component Interconnect”) interface for receiving data,e.g., video data, from another PCM or from the external to themotherboard 100 through input structure 102, and/or transmitting suchdata to another PCM or to the external of the motherboard 100 throughoutput structure 104.

Connected to each PCM 106, 108 is a respective Advanced Video Encoder™(“AVE”) card 110 (Advanced Video Encoder™ is a trademark of SkyStreamCorporation, assignee of the present application). The AVE card 110includes a plurality of DSPs 112 a-112 c for running computer softwarefor performing a variety of functions including, but not limited to, theadvantageous methods of the present invention. Each AVE card 110 furtherincludes memory 114, connected to the DSPs 112 a-112 c via a busstructure 116. Memory 114 is adapted to store data including, but notlimited to, video data pending pre-processing, noise-filtering and/orencoding in accordance with the present invention, and video data thathas been pre-processed, noise-filtered and/or encoded in accordance withthe present invention, or both. Accordingly, the DSPs 112 a-112 cconstitute structure for performing the various steps of any of themethods in accordance with the present invention.

It will be understood, of course, that any other effective structure,such as application specific integrated circuits or finite stateautomata, may be used in place of the above-described structures forimplementing the present invention.

Advantageous methods and signals in accordance with preferredembodiments of the present invention that may be implemented using themotherboard 100, other structure properly incorporating AVE cards 110,or other effective structure will now be described.

1. Fast Motion Estimation For Temporal Pre-Processing

The present invention is directed to a fast motion estimation approachfor video temporal pre-processing. Advantageously, an embodiment of thisinvention consists of one or more of three major portions: the selectionof the distortion function criterion, the method of motion vectorprediction, and an optimal fast search scheme, presented in thefollowing sections.

1.2 The Distortion Function Criterion For High Quality Images

The distortion function criterion is the criterion for identifying whichof the candidate prediction macroblocks is the “best match.”Consequently, defining the distortion function criterion is a veryimportant part of a motion estimation method in terms of computationalcomplexity and performance. The goal of motion estimation schemes is toreach the global minimum of a well-defined distortion function criterionin a fast and efficient way.

One conventional criterion, the sum of absolute differences (SAD), isgiven by:

${SAD} = {\sum\limits_{i = 0}^{15}{\sum\limits_{j = 0}^{15}{{C_{ij} - R_{ij}}}^{2}}}$where C_(ij) and R_(ij) denote the current and reference image intensityat spatial location (i, j), respectively.

The SAD function is a widely used distortion function criterion in thevideo compression domain for its low and practical complexity andrelatively good quality for video coding. However, in a pre-processingoperation like noise reduction for coding efficiency, the SAD functionmight not be a good choice due to the noisy input video data or/andreconstructed blocky image sequences in encoding and transcodingenvironments, respectively. The SAD function weights the error uniformlyand its function is easily affected by input distortion like noise dataand blocky image due to compression loss.

The SAD function is a first-order statistical function, i.e., the termsin the sum are first-order. On the other hand, general image/videopre-processing algorithms are mostly derived from optimization on thebasis of second-order statistical characteristics and features likeimage data energy. The present invention recognizes that selecting asecond-order distortion function can improve results by usingcomplementary characteristics.

In accordance with the present invention, the distortion functioncriterion J(Δx, Δy) is proposed to be the mean squared error (MSE),advantageously in combination with entropy Lagrange optimization.

${MSE} = {\frac{1}{N \cdot M}{\sum\limits_{i = 0}^{N - 1}{\sum\limits_{j = 0}^{M - 1}\left( {C_{ij} - R_{ij}} \right)^{2}}}}$J  (Δ x, Δ y) = MSE  (Δ x, Δ y) + λ ⋅ E  (Δ x, Δ y),

where E(Δx, Δy) is the entropy of the image sequence.

E(Δx, Δy) is defined as follows:

${{E\mspace{11mu}\left( {{\Delta\; x},{\Delta\; y}} \right)} = {{- {\sum\limits_{i = 0}^{N - 1}{\sum\limits_{j = 0}^{M - 1}{p_{ij}\log_{2}p_{ij}}}}} - {q_{ij}\log_{2}q_{ij}}}},$

where

-   -   p_(ij) is the probability of the pixel value at the location        (i,j) of the relevant macroblock in the reference image, and    -   q_(ij) is the probability of the pixel value at the location        (i,j) of the relevant macroblock in the current image.

The probabilities p_(ij) and q_(ij) can be calculated according to oneof several techniques. For example, the sample (luminance andchrominance) values of each pixel can be detected in a given image.Then, a probability distribution of the possible pixel values can beconstructed using all of the sample values measured in that given image.Alternatively, the sample value of a specific pixel location i,j can bedetected over a sequence of images. A probability distribution of thepossible pixel values can then be constructed over time (i.e., over thesequence of images) for that specific pixel position i,j. Such atemporal probability distribution can be constructed for each pixelposition. Illustratively, an auto-regression technique may be employedto generate such a temporal probability distribution. In the case of thereference image, it might be useful to use the sample values of thepixel after noise filtering, in the case the motion estimation techniqueis used in a noise filtering pre-processing application, or to use thedecoded, reconstructed sample values, in the case the motion estimationtechnique is used in a compression application.

The MSE distortion function criterion fits naturally into thepre-processing optimization operations because the pre-processingfilters are derived based on this criterion. Accordingly, a motionestimation scheme in accordance with the present invention that alsouses this criterion would perform well with such pre-processing filters.

From another perspective, the idea behind the MSE distortion functioncriterion is to always apply the temporal filter in the direction ofhighest correlation. The motion direction is chosen by searching over arange, and the direction with the minimum variance estimation isselected.

Using the MSE distortion function criterion is also very handy forvariable block-size operations as in MPEG 2 to H.264 transcoding orencoding. In the case that both N and M are powers of 2, which isusually the case in video compression, the division can be done by aright shift, which is ideal for computational implementation. Due to thecurrent processing platforms, multiplication is no longer an expensiveoperation. For instance, most current powerful DSP processors use onecycle for a multiplication operation with one-cycle latency which couldbe covered by pipelined operations. A multiplication operation can befaster than an absolute operation on most processor platforms.

The entropy estimation can be a bit expensive. Therefore, for a simple,fast and practical implementation, a simplified optimization can be usedas follows:Min(J(Δx, Δy))=Min(MSE(Δx, Δy)).

Even this motion estimation can be used with the encoding or transcodingprocess to get an improved combined result of pre-processing andencoding (or transcoding) optimizations in terms of visual quality andcompression ratio.

Both video pre-processing filtering and CODEC compression target imagesequence entropy reduction by achieveing noise reduction and redundancyreduction, respectively. The J(Δx, Δyx) function is a very good combinedoptimization criterion.

If there is no information loss, the allocated bit rate must meet thefollowing condition:E(Δx, Δy)≦R(Δx, Δy).

In reality, the bit rate target is usually set in advance for certainapplications or markets, so there is a certain amount of room forentropy reduction. The entropy estimation can also use this guideline.

1.3 Motion Prediction For Efficient Initialization

Once the distortion function criterion is chosen, the motion estimationscheme starts with a motion vector prediction using neighboring blocksand blocks on adjacent frames with already computed motion vectors asthe initialization of the pattern search. The scheme uses the motionvectors estimated from the following blocks:

9. The block at the top-left: MV_(x−1,y−1)

10. The block directly at the left: WV_(x−1,y)

11. The block directly at the top: MV_(x,y−1)

12. The block at the top-right: MV_(x+1,y−1)

13. The co-located block in the previous frame: MV_(x,y)(k−1)

14. The function (e.g., median, average etc.) of a combination of abovepositions:MV _(x,y)=MEDIAN(MV _(x−1,y−1) , MV _(x−1,y) , . . . , MV _(x,y)(k−1)).

The best prediction with minimum MSE distortion is chosen as the initialstarting point for the fast motion estimation search.

The bilinear average of the top and left motion vectors is a usual andpractical motion vector prediction choice.

In a practical implementation of the present invention, a direct choicefrom the above motion vectors can be made without any comparison. On thetop border, the motion vector of the left block can be used. On the leftborder, the motion vector of the top block can be chosen. At thetop-left position of a frame, the co-located block motion vector can bechosen.

1.4 Fast Search Method For Optimal Performance

After the motion vector prediction initialization, a second aspect ofthe present invention is found in a fast search that starts from theposition of the motion prediction result. The first search step(iteration) in accordance with the present invention will be a diamondsearch as depicted in FIG. 1. The diamond search is based on the motionvector field distributions of statistical characters of video sequences.It is derived from the probability distribution function with theselocations corresponding to the highest probability of finding thematching block in the reference frame. It offers the advantage ofextending the search support area at the first search iteration,allowing more reference frame coverage with fewer position computations.

The sparse nature of the diamond is a good way to approach the optimumneighborhood in a relatively faster fashion. However, the search needsto be made finer with the following nearest neighbor search, and theiteration of a diamond search is not necessary. Indeed, it could beharmful by jumping around.

The following nearest neighbor search is shown in FIG. 2, and startswith MSE computations at the four nearest positions around the currentposition. Once the minimum MSE location is found and defined as the“best” location for this iteration, this best location is used as thenew center and a new nearest neighbor search iteration is executed untilno better position in terms of the MSE distortion function criterion hasbeen found.

This proposed fast search scheme has a much lower computationalcomplexity than a full search, but very closely approaches theperformance of one.

For variable block sizes of matching block and variable sub-pixel motionvector accuracy, this motion estimation can be used without any change,which can be a huge advantage in the implementation speed-up. Forinstance, the motion estimation procedure integrates all computations ofdifferent block sizes with any conditional decisions and branching jumpswhich could result in pipeline flushing on parallel DSP platforms.Sub-pixel accuracy can be achieved with on-the-fly sub-pixelinterpolations of nearest neighbor up-level pixels (points) without fullframe sub-pixel interpolations, i.e., search and interpolation isperformed in only the area that is needed.

The interpolation can be a simple bilinear interpolation, a six-tapfiltering interpolation like the half-pixel interpolation in H.264, orany other suitable interpolation technique.

In order to get consistent and high subjective quality motion estimationresults and avoid unnecessary computation, the idea of an adaptivemotion search range cap is introduced. The motion range cap is adaptedto the search block neighborhood environment to be, for instance, Beta(β) times the maximum or the arithmetic average of the neighboring blockmotion vectors.

For example, if the maximum is chosen, then:M _(CAP)=β*Max(MV _(x−1,y−1) , MV _(x−1,y) , . . . , MV _(x,y)(k−1)),

where β can be an adaptive value or a constant, such asβ=(1+Max(MV _(x−1,y−1) , MV _(x−1,y) , . . . , MV _(x,y)(k−1)),or

β=a constant in the range of 1.5 to 5, preferably equal to 2 or 3.

The introduction of an adaptive motion search range cap mechanismenables the present invention to deal gracefully, efficiently andsmartly with such events and an incoming new object or other content, aflat, smooth area and a fast motion scenario.

2. Motion Estimation Scheme Implementation

The implementation of the proposed motion estimation scheme inaccordance with the present invention can be very efficient on high-endVLIW and SIMD DSP platforms. The arithmetic operations can be carriedout in parallel fashion. The data flow and current and reference blockdata have to be effectively architected and arranged based upon thespecific processor architectures.

As noted above, the search range is determined dynamically andadaptively using the the adaptive motion search range cap M_(CAP).Advantageously, a pre-defined maximum search range cap can beestablished providing an upper limit for M_(CAP). Examples of the upperlimit vary depending on the application, but advantageous examples rangefrom 2048×2048 to 16×16, with 64×64 being a useful example. Similarly,the block size can vary depending on the application, with advantageousblock choices being, for example, 16×16, 16×8, 8×16, 8×4, 4×8 and 4×4.The block size can also vary dynamically depending on one or severalfactors according to the search requirements.

At each step in the search procedure, the calculated positions should bestored to avoid the recalculation of the same positions in the nextsearch step.

In order to get high visual quality, sub-pixel motion vector accuracycan be achieved by bilaterally interpolating near the neighbors of thesearched best integer pixel position on-the-fly. The motion accuracy canbe integer pixel, half-pixel and quarter-pixel without requiring wholeframe interpolations at half- or quarter-pixel levels.

3. Computational Complexity And Performance

By comparison with the full search motion estimation method, theproposed motion estimation scheme should be much faster at the integerpixel level, for instance, at least 40 times faster for a 64×64 integerpixel search range. Even faster results will show at sub-pixel levels.

Taking the L×S search range as an example, the total computationalcomplexity for a full search algorithm is the number of positions atinteger pixel level, N×M, times the distortion computation for eachblock.Complexity\full=L×S×MSE.

The proposed scheme's complexity is the computation of motion prediction(fewer than five arithmetic operations), plus eight positions in thediamond search and a maximum of three positions in each nearest neighborsearch step multiplied by the step number (the maximum is half of longerside of the search range), then times the distortion computation foreach block:Complexity\proposed≦5+(8+3×StepNumber)×MSE,where

${StepNumber} \leq {\frac{L}{2}.}$

The performance of the proposed scheme approaches that of the fullsearch method. In experiments, the performances of both methods are veryclose in terms of subjective and objective measurements.

4. Motion Estimation Applications

Motion estimation is used as a major component in many video processingand compression areas. Indeed, it constitutes most of the computationinvolved in many solutions such as encoding, transcoding and videoenhancements. Therefore a faster and better performing motion estimationwould significantly contribute to video processing and compressionsolutions.

The motion estimation in accordance with the present invention can beused separately for both video pre-processing and video coding. However,it can also be used for both in cascaded pre-processing and codingstages at the same time in a system, which would result in additionalmajor resource savings in terms of computation, memory and data transferfor a very efficient and fast system implementation.

4.2 Pre-Processing Noise Filtering

The motion estimation search technique can be employed in a noisefilter, e.g., applied in a pre-processing stage prior to encoding. Anytype of motion estimated type of noise filtering can be employed withthe motion estimation search technique of the invention. In anillustrative technique, a recursive 1-D temporal filter is applied to acurrent image (e.g., a frame picture) using a past image (e.g., aframe). For each block or macroblock of the current image to befiltered, a reference block or macroblock in the past, already filteredimage is detected using the above-described motion estimation technique.Then, the following recursive 1-D temporal filter is applied to theblock or macroblock of the current image to be filtered and the detectedreference block or macroblock:{circumflex over (f)}(i.j,k)=(1−α(i,j,k))•{circumflex over (f)}_(b)(i.j,k)+α(i,j,k)•g(i,j,k)where:

-   {circumflex over (f)}(i,j,k) is an output filtered pixel at x,y    coordinates i,j in an image of time t=k-   {circumflex over (f)}_(b)(i,j,k) is a previously filtered pixel at    x,y coordinates i,j in the image of time t=k, i.e., the output    filtered pixel at x,y coordinates i,j in the image of time t=k−1-   g(i,j,k) is the current input pixel of the block or macroblock    undergoing filtering-   α(i,j,k) is determined as:

${\alpha\mspace{11mu}\left( {i,j,k} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{11mu}{{{g\mspace{11mu}\left( {i,j,k} \right)} - {\hat{f}\mspace{11mu}\left( {i,j,{k - 1}} \right)}}}} \geq \tau} \\\alpha_{g} & {{{if}\mspace{11mu}{{{g\mspace{11mu}\left( {i,j,k} \right)} - {\hat{f}\mspace{11mu}\left( {i,j,{k - 1}} \right)}}}} < \tau}\end{matrix} \right.$

-   α_(g) is determined as:

$\alpha_{g} = {1 - {\exp\mspace{11mu}\left\{ \frac{{{g\mspace{11mu}\left( {i,j,k} \right)} - {\hat{f}\mspace{11mu}\left( {i,j,{k - 1}} \right)}}}{\beta} \right\}^{\gamma}}}$

-   β, γ and τ are configurable parameters.    In the case of a first image of a sequence, noise filtering can be    omitted, or some non-temporal form of noise filtering (which does    not require a previous image) can be applied.

4.3 Pre-Processing Motion Estimation For An Encoder

In encoding environments, pre-processing provides encoding gain andvisual enhancement by improving the quality of source-captured videosequences.

In the pre-processing stage, a spatial filter will smooth an intra imagefor noise reduction and visual improvement and a temporal filter willenhance a current image by filtering across several neighboring interimages. If the temporal filer follows a spatial filter, the smoothingeffect by the spatial filter makes the second order moment criterionmore important for the motion estimation in the following temporalfiltering. The temporal filter would work better with the better motiondirection estimation to preserve strong edges while filtering outnoises.

Meanwhile, noise reduction by pre-filtering would help the encoder usethe bit budget for real information in the image sequence by bothreducing the variances of image or image difference amplitudes and byproviding better motion estimation/compensation.

However, the motion estimation results from the pre-processing temporalfiltering and the encoder should be almost identical if both motionestimation procedures use the same distortion function—especially ifthat distortion function is the MSE criterion. In the case of using thesame distortion function, the encoder at the later stage can use theresults of motion estimation from the pre-processing at the earlierstage as long as the blocks and their positions are the same. Putanother way, the encoder can use the motion estimation of thepre-processing to speed up and improve its motion estimation andcompensation.

4.4 Pre-Processing Motion Estimation for a Transcoder

In transcoding environments, pre-processing provides encoding gain andvisual enhancement by improving the quality of video sequences corruptedin the compression, transmission or storage processes. The benefitsmentioned in the previous section for the present invention stay truefor the transcoding environment, except that the transcoding procedureincludes a decoding stage before the pre-processing stage, and theencoding afterwards would be different from the previous video decodingmodel due to different video compression standards/algorithms.

In the transcoding environment, pre-processing in accordance with thepresent invention can play an even more important role which not onlyreduces the noise from the compressed video transmission or storage butalso smoothes blocky artifact or compression distortion from thecompression (coding and decoding) process. The spatial filtering is veryeffective in smoothing the block artifacts in block-based compressionalgorithms like MPEGs and H.26x, which is also very helpful for motionestimation in later stages.

Also, in the transcoding process, motion vectors obtained in thedecoding stage can be used as an approximation and initial start pointcandidates in the motion prediction stage in accordance with the presentinvention.

5. Summary

Thus, a comprehensive pre-processing motion estimation scheme has beenpresented, including but not limited to the following innovative areas:

15. The combination and process of three major components: distortioncriterion, motion prediction, and hybrid search.

16. The optimization object function J(Δx, Δy) and MSE used incalculations for: optimal visual quality, unique application in variableblock size encoding in an encoder or transcoder, entropy calculationcombined with encoding bit rate control process, faster computation andimplementation.

17. The new three-level motion search process from coarse to fine:motion vector prediction as efficient initialization, first-step diamondsearch, and on-the-fly nearest neighbor search, which yields fasterresults than a logarithmic value of the search range and optimalperformance similar to the full search.

18. The unique idea of entropy reduction in motion estimation and itsimplementation.

19. The new idea and implementation of the combined motion estimationfor both pre-processing and encoding/transcoding.

20. The on-the-fly neighboring motion (search) range adaptation to get aconsistent and high quality motion result and avoid unnecessarycomputations.

21. The new on-the-fly nearest neighbor search with on-the-fly pixelinterpolation for high motion vector accuracy at sub-pixel (fractionalvalue) levels for high quality video compression and processingapplications/markets.

22. The DSP implementation and optimization, memory architecture anddata flow, and other optimal implementation details.

Although the invention has been described in its preferred forms with acertain degree of particularity, obviously many changes and variationsare possible therein. It is therefore to be understood that the presentinvention may be practiced other than as specifically described hereinwithout departing from scope and the sprit thereof.

1. A method of fast motion estimation for video pre-processing, comprising the steps of: (A) selecting, using a microprocessor, a second-order distortion criterion for identifying a best reference image portion for comparison with a current image portion, said second-order distortion criterion being a Lagrange-optimized combination of a mean squared error criterion with an entropy criterion; (B) using said second-order distortion criterion to select, using a microprocessor, an initial reference image portion as an initial starting point for a motion estimation search, said initial starting position being chosen based on at least one motion vector from a neighboring image portion; and (C) performing, using a microprocessor, a fast motion estimation search including the steps of: (i) performing, using a microprocessor, a diamond search starting from said initial reference image portion using said second-order distortion criterion to identify a candidate best reference image portion; (ii) performing, using a microprocessor, a nearest neighbor search starting from the candidate best reference image portion using said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap; (iii) re-identifying, using a microprocessor, the better reference image portion as the candidate best reference image portion; (iv) repeating, using a microprocessor, said steps (ii) and (iii) until in said step (ii) either (a) the candidate best reference image portion is identified as the better reference image portion so that said second-order distortion criterion cannot be improved, or (b) any further search would exceed the adaptive search range cap; (v) identifying, using a microprocessor, the candidate best reference image portion as the best reference image portion; and (vi) calculating, using a microprocessor, a motion vector from the best reference image portion.
 2. A method of fast motion estimation for video pre-processing, comprising the steps of: (A) selecting, using a microprocessor, a second-order distortion criterion for identifying a best reference image portion for comparison with a current image portion, said second-order distortion criterion being a Lagrange-optimized combination of a mean squared error criterion with an entropy criterion; and (B) using said second-order distortion criterion to select, using a microprocessor, an initial reference image portion as an initial starting point for a motion estimation search, said initial starting position being chosen based on at least one motion vector from a neighboring image portion. (C) performing, using a microprocessor, a fast motion estimation search including the steps of: (i) performing, using a microprocessor, a diamond search starting from said initial reference image portion to identify a candidate best reference image portion; (ii) performing, using a microprocessor, a nearest neighbor search starting from the candidate best reference image portion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range; (iii) re-identifying, using a microprocessor, the better reference image portion as the candidate best reference image portion; (iv) repeating, using a microprocessor, said steps (ii) and (iii) until in said step (ii) either (a) the candidate best reference image portion is identified as the better reference image portion so that said second-order distortion criterion cannot be improved, or (b) any further search would exceed the adaptive search range cap; and (v) identifying, using a microprocessor, the candidate best reference image portion as the best reference image portion.
 3. The method of claim 2, wherein said step of performing a diamond search starting from said initial reference image portion uses said second-order distortion criterion to identify a candidate best reference image portion.
 4. The method of claim 3, wherein said step of performing a nearest neighbor search starting from the candidate best reference image portion uses said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within the search range.
 5. The method of claim 4, wherein said step of performing a nearest neighbor search starting from the candidate best reference image portion uses said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap.
 6. The method of claim 2, wherein said step of performing a nearest neighbor search starting from the candidate best reference image portion uses said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within the search range.
 7. The method of claim 6, wherein said step of performing a nearest neighbor search starting from the candidate best reference image portion uses said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap.
 8. The method of claim 7, further comprising the step of: (vi) calculating, using a microprocessor, a motion vector from the best reference image portion identified in step (v).
 9. The method of claim 6, wherein said step of performing a nearest neighbor search starting from the candidate best reference image portion identifies either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap.
 10. The method of claim 9, further comprising the step of: (vi) calculating, using a microprocessor, a motion vector from the best reference image portion identified in step (v).
 11. Apparatus for performing fast motion estimation for video pre-processing, comprising: (A) structure for selecting a second-order distortion criterion for identifying a best reference image portion for comparison with a current image portion, said second-order distortion criterion being a Lagrange-optimized combination of a mean squared error criterion with an entropy criterion; (B) structure for using said second-order distortion criterion to select an initial reference image portion as an initial starting point for a motion estimation search, said initial starting position being chosen based on at least one motion vector from a neighboring image portion; and (C) structure for performing a fast motion estimation search including: (i) structure for performing a diamond search starting from said initial reference image portion using said second-order distortion criterion to identify a candidate best reference image portion; (ii) structure for performing a nearest neighbor search starting from the candidate best reference image portion using said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap; (iii) structure for re-identifying the better reference image portion as the candidate best reference image portion; (iv) wherein the nearest neighbor search and the re-identification are repeated until, in performing the nearest neighbor search, either (a) the candidate best reference image portion is identified as the better reference image portion so that said second-order distortion criterion cannot be improved, or (b) any further search would exceed the adaptive search range cap; (v) structure for identifying the candidate best reference image portion as the best reference image portion; and (vi) structure for calculating a motion vector from the best reference image portion.
 12. A method of statistical content block matching for video pre-processing, comprising, in the recited order, the steps of: First: (A) selecting, using a microprocessor, a second-order distortion criterion for identifying a best reference image portion for comparison with a current image portion, said second-order distortion criterion being a Lagrange-optimized combination of a mean squared error criterion with an entropy criterion; Second: (B) using said second-order distortion criterion to select, using a microprocessor, an initial reference image portion as an initial starting point for a motion estimation search, said initial starting position being chosen based on at least one motion vector from a neighboring image portion; and Third: (C) performing, using a microprocessor, a fast motion estimation search including the steps of: Fourth: (i) performing, using a microprocessor, a diamond search starting from said initial reference image portion using said second-order distortion criterion to identify a candidate best reference image portion; Fifth: (ii) performing, using a microprocessor, a nearest neighbor search starting from the candidate best reference image portion using said second-order distortion criterion to identify either the candidate best reference image portion or a different neighboring reference image portion as a better reference image portion within a search range limited by an adaptive search range cap; Sixth: (iii) re-identifying, using a microprocessor, the better reference image portion as the candidate best reference image portion; Seventh: (iv) repeating, using a microprocessor, said steps (ii) and (iii) until in said step (ii) either (a) the candidate best reference image portion is identified as the better reference image portion so that said second-order distortion criterion cannot be improved, or (b) any further search would exceed the adaptive search range cap; Eighth: (v) identifying, using a microprocessor, the candidate best reference image portion as the best reference image portion; and Nineth: (vi) calculating, using a microprocessor, a motion vector from the best reference image portion. 